The field of dentistry lacks satisfactory tools to help visualize planned procedures and their potential results to patients. Dentists struggle to provide an effective image in their patient's mind of the end results of the planned treatment only through verbal explanations. Thus, verbal explanations alone often cannot adequately help the patients make a treatment decision. Inadequate attempts are frequently made by dentists to sketch the procedure for the patient in an effort to depict the treatment. These attempts however require an artistic ability not all dentists have. Real case photographs are sometimes of help in explaining and illustrating treatments. However, particularly in implant cases, real case photographs are often ineffective and inadequate. The purpose of this study is to develop a mobile application with an effective user interface design to support the dentist-patient interaction by providing the patient with illustrative descriptions of the procedures and the end result. Sketching, paper prototyping, and wire framing were carried out with the actual user's participation. Hard and soft dental tissues were modeled using three dimensional (3D) modeling programs and real cases. The application enhances the presentation to the patients of potential implants and implant supported prosthetic treatments with rich 3D illustrative content. The application was evaluated in terms of perceived ease of use and perceived usefulness through an online survey. The application helps improve the information sharing behavior of dentists to enhance the patients' right to make informed decisions. The paper clearly demonstrates the relevance of interactive communication technologies for dentist-patient communication.

Human activity recognition (HAR) by deep learning is a challenging and interesting topic. Although there are robust models, there is also a bunch of parameters and variables, which affect the performance such as the number of layers, pooling type. This study presents a new deep learning architecture that is obtained by fine-tuning of the conventional CNN-LSTM model, namely, CNN (+3)-LSTM. Three changes are made to the conventional model to increase the accuracy. Firstly, kernel size is set to 1×1 to extract more information. Secondly, three convolutional layers are added to the model. Lastly, average pooling is used instead of max-pooling. Performance analysis of the proposed model is conducted on the KTH dataset and implemented on Keras. In addition to the overall accuracy of the proposed model, the contribution of each change is observed individually. Results show that adding layers made the highest contribution followed by kernel size and pooling, respectively. The proposed model is compared with state-of-art and outperformed some of the recent studies with a 94.1% recognition rate.

Reciprocal recommendation requires satisfying preferences on both sides of a match, which differs from standard one-sided settings and often involves hierarchical structure (e.g., skills, seniority, education). We present biLorentzFM, which is a multi-objective framework that integrates hyperbolic geometry into factorization machine architectures using Lorentz embeddings with learnable curvature and manifold-aware optimization. The approach addresses whether a geometric structure aligned with hierarchical relationships can improve reciprocal matching without requiring major architectural changes. On a large-scale recruitment dataset from Kariyer.Net (1,150,302 interactions, 229,805 candidates), the model achieves candidate and company AUCs of 0.9964 and 0.9913 respectively, representing 6.6% and 6.0% improvements over the strongest Euclidean baseline while maintaining practical inference latency (2.1 ms per batch). Cross-validation analysis confirms robustness (5-fold: 0.9813 ± 0.0002; 3-seed: 0.9964 ± 0.0012) with very large effect sizes (Cohen’s d = 2.89–3.08). Although the per-epoch training time increases by 23.5% due to manifold operations, faster convergence (12 vs. 18 epochs) reduces the total training time by 17.8%. Cross-domain evaluation on Speed Dating data demonstrates generalization beyond explicit hierarchies with a 2.8% AUC improvement despite lacking structured taxonomies. Learned curvature parameters differ by entity type, providing interpretable indicators of hierarchical structure strength. Ablation studies isolate contributions from geometric structure (6.6%), learnable curvature (4.7%), multi-objective learning (2.1%), and explicit feature interactions (0.6%). A systematic comparison reveals that Lorentz embeddings outperform Poincaré ball implementations by 4.4% AUC under identical conditions, which is attributed to numerical stability advantages. The results indicate that pairing standard recommendation architectures with geometry reflecting hierarchical relationships can provide consistent improvements for reciprocal matching, while limitations including cold-start performance, computational overhead at an extreme scale, and static hierarchy assumptions suggest directions for future work on adaptive curvature, fairness constraints, and dynamic taxonomies.

The growing complexity of agricultural ecosystems increasingly requires automated systems that can support real-time, data-driven decision-making. In response to this need, this paper presents a multimodal data fusion framework aimed at enhancing intelligent farming systems by integrating heterogeneous information sources, including IoT sensor streams (soil N, P, K, pH) and historical datasets from the FAO. The proposed Two-Layer Analytics Architecture combines derived event generation with probabilistic fusion mechanisms to address challenges related to scalability, data heterogeneity, and operational responsiveness. At the core of the framework, we adopt an ensemble learning strategy based on Random Forest, XGBoost, and Support Vector Machines (SVM) to classify crop suitability and soil conditions. Experimental evaluations indicate that this fusion-based approach yields a weighted F1-score improvement of 6 % compared to single-source, single-model baselines. Furthermore, the overall system architecture is validated in an edge computing context, where it demonstrates linear scalability with a processing throughput of 10⁴ events per second while maintaining fusion latency consistently below 100 ms. These results highlight the transformative potential of multimodal data fusion in enabling high-precision, resilient, and sustainable agricultural automation.